Method of predicting suitability for a crop harvesting operation

FIELD OF THE INVENTION

0001The present invention relates to the prediction of crop conditions and assessment of suitability for performance of a crop harvesting operation.

BACKGROUND OF THE INVENTION

0002Land engaged in agriculture is subjected to a number of crop harvesting operations. In order to optimize performance of these operations for efficiency, minimal crop loss, and/or minimal impact on the soil, it is critical that harvest operations be performed when crop, weather, and soil conditions are suitable. In order to aid in planning, a method of predicting suitable times for performing a number of different crop harvesting operations is desirable.

SUMMARY OF THE INVENTION

0003Presented herein is a method for predicting suitable times for performing a crop harvesting operation. The method includes the steps of accessing predicted values for weather, crop, and soil conditions, and then predicting one or more values for soil characteristics, operation characteristics, and operation effects. Based on these predicted operation variables and selected suitability parameters, the method predicts harvest operation suitability for different points in time.

BRIEF DESCRIPTION OF THE DRAWINGS

0004<figref idref="DRAWINGS">FIG. 1</figref> illustrates a farm field having many field nodes.

0005<figref idref="DRAWINGS">FIG. 2</figref> illustrates a first embodiment for the present invention method.

0006<figref idref="DRAWINGS">FIG. 3</figref> illustrates a second embodiment for the present invention method.

0007<figref idref="DRAWINGS">FIG. 4</figref> illustrates a table displaying suitability values for performance of a crop harvesting operation at a single field node on a single day.

0008<figref idref="DRAWINGS">FIG. 5</figref> illustrates a map displaying suitability values for performance of a crop harvesting operation over a single field on a single day.

0009<figref idref="DRAWINGS">FIG. 6</figref> illustrates a graphical displaying suitability values for performance of a crop harvesting operation over a single field for multiple days.

0010<figref idref="DRAWINGS">FIG. 7</figref> illustrates a graphical displaying suitability values for performance of a crop harvesting operation over multiple fields on a single day.

DETAILED DESCRIPTION

0011<figref idref="DRAWINGS">FIG. 1</figref> illustrates a parcel of land, or field <b>10</b>, suitable for agricultural use, and under agricultural cultivation. As such, the field <b>10</b> may be subjected to crop harvesting operations such as mechanized mowing and combining, as well as human handpicking and animal foraging. Numerous field nodes <b>12</b> dispersed throughout field <b>10</b> divide the parcel into smaller sample areas. A method presented herein predicts suitability <b>6</b> for performing such operations in the field <b>10</b> at different points in time, based on operation variables <b>8</b> predicted for each field node <b>12</b>.

0012<figref idref="DRAWINGS">FIG. 2</figref> illustrates a first embodiment <b>20</b> of the present invention whereby the method predicts operation variables <b>8</b> indicative of operation performance suitability <b>6</b> at field node <b>12</b>. The first step <b>22</b> in this embodiment <b>20</b> is to access values predicted for weather conditions <b>24</b> at the node <b>12</b>. These predicted weather conditions <b>24</b> include values for, but are not limited to, temperature, relative humidity, wind speed, precipitation, and solar radiation. Values for these conditions <b>24</b> can be obtained from sources such as the National Weather Service website, operated by the National Oceanic and Atmospheric Administration.

0013The second step <b>102</b> in this embodiment <b>20</b> is to access values predicted for crop conditions <b>104</b> at the node <b>12</b> at different points in time. These crop conditions <b>104</b> include, but are not limited to, crop maturity level and crop moisture content. The third step <b>26</b> in this embodiment <b>20</b> is to access values predicted for soil conditions <b>28</b> at the node <b>12</b> at different points in time. These soil conditions <b>28</b> include, but are not limited to, soil moisture and soil temperature. To predict values for both crop conditions <b>104</b>, and soil conditions <b>28</b>, the method may use a dynamic soil model, such as the Precision Agricultural-Landscape Modeling System (PALMS) developed under NASA's Regional Earth Science Application Center (RESACA) program. This program predicts crop maturity and moisture, and soil moisture and temperature, as well as other variables, based on predicted weather conditions, measured soil conditions, and crop season parameters. This computer program is available under license for research or commercial use through the Wisconsin Alumni Research Foundation.

0014The fourth step <b>30</b> in this embodiment <b>20</b> is to select a soil profile <b>32</b> representative of the field node <b>12</b>. A soil profile <b>32</b> describes a particular soil for which empirical tests have been conducted for this method <b>20</b>. A soil profile <b>32</b> includes information such as soil type and composition, down to several feet. The fifth step <b>34</b> is to select an operation profile <b>36</b> representative of the crop harvesting operation to be performed. An operation profile <b>36</b> describes a particular operation for which empirical tests have been conducted for this method <b>20</b>. Operation profiles <b>36</b> include parameters such as operation type, equipment size, machine configuration, and operation speed. The operation profile <b>36</b> might also include additional parameters such as crop species and fuel price.

0015The sixth step <b>38</b> in this embodiment <b>20</b> is to predict operation characteristics <b>40</b> that are resultant upon performance of the operation under the predicted soil conditions <b>28</b>. Operation characteristics <b>40</b> are generally indicative of operation suitability <b>6</b>, and include, but are not limited to, soil compaction impact (Δ compaction), soil particle size, tractive efficiency, and fuel consumption. In the illustrated embodiment <b>20</b>, these operation characteristics <b>40</b> are determined by referring to empirical tables <b>42</b> giving values for known soil conditions <b>28</b>, soil profile <b>32</b>, and operation profile <b>36</b>. For example, a table <b>42</b> giving values for Δ compaction may be developed by performing the crop harvesting operation under a number of soil moisture conditions on a test plot having a consistent soil composition. The parameters of the harvesting operation performed define the operation profile <b>36</b>, and the composition of the test plot soil defines the soil profile <b>32</b>.

0016The seventh step <b>44</b> in this embodiment <b>20</b> is to predict operation effects <b>46</b> that are resultant upon performance of the operation, given the predicted operation characteristics <b>40</b>. Operation effects <b>46</b> are also indicative of operation suitability <b>6</b>, and include, but are not limited to, crop yield impact and fuel cost. In the illustrated embodiment <b>20</b>, these effects <b>46</b> are determined by referring to empirical tables <b>48</b> giving values for known operation characteristics <b>40</b>, soil profile <b>32</b>, and operation profile <b>36</b>. For example, a table <b>48</b> giving values for crop yield impact may be developed by measuring crop yields under a number of soil compaction levels on a test plot having a consistent soil composition. Examples outlining the development of such tables <b>48</b> may be found in <i>Soybean Growth and Yield as Affected by Subsurface and Subsoil Compaction</i>, J. F. Johnson, et al., Agronomy Journal, Vol. 82, No. 5, September–October 1990.

0017<figref idref="DRAWINGS">FIG. 3</figref> illustrates a second embodiment <b>21</b> of the present invention whereby the method predicts operation variables <b>8</b> indicative of operation performance suitability <b>6</b> at a node <b>12</b> within the field <b>10</b>. The first step <b>22</b>′ in this embodiment <b>21</b> is to access values predicted for weather conditions <b>24</b> at the node <b>12</b>, like the first embodiment <b>20</b>. The second step <b>102</b>′ in this embodiment <b>21</b> is to access values predicted for crop conditions <b>104</b> at the node <b>12</b> at different points in time, as in the first embodiment <b>20</b>. The third step <b>26</b>′ in second embodiment <b>21</b> is to access values predicted for soil conditions <b>28</b> at the node <b>12</b> at different points in time, like the first embodiment <b>20</b>. The fourth step <b>30</b>′ in this embodiment <b>21</b> is to select a soil profile <b>32</b> representative of the field node <b>12</b>, like the first embodiment <b>20</b>.

0018The fifth step <b>50</b> in this embodiment <b>21</b> is to predict values for soil characteristics <b>52</b> for a soil under known soil conditions <b>28</b>. The soil characteristic <b>52</b> of particular interest in this embodiment is Atterberg Limits. These soil characteristics <b>52</b> are determined in the illustrated embodiment <b>21</b> by referring to empirical tables <b>54</b> giving values for known soil conditions <b>28</b> and soil profile <b>32</b>. These tables <b>54</b> may be generated by performing tests under a number of soil moisture conditions on specimens of soil profiles <b>32</b> according to <i>ASTM D </i>4318-00<i>: Standard Test Method for Liquid Limit, Plastic Limit, and Plasticity index of Soils. </i>

0019The sixth step <b>34</b>′ in this embodiment <b>21</b> is to select an operation profile <b>36</b> representative of the crop harvesting operation. The seventh step <b>38</b>′ in this embodiment <b>21</b> is to predict operation characteristics <b>40</b> that are resultant upon performance of the operation, given the predicted soil characteristics <b>52</b>. In the illustrated embodiment <b>21</b>, these operation characteristics <b>40</b> are determined by referring to empirical tables <b>56</b> giving values for known soil characteristics <b>52</b>, soil profile <b>32</b>, and operation profile <b>36</b>. For example, a table <b>56</b> giving tractive efficiency and fuel consumption may be developed empirically by performing the crop harvesting operation under a number of Atterberg Limit conditions.

0020The eighth step <b>44</b>′ in this embodiment <b>21</b> is to predict operation effects <b>46</b> that are resultant upon performance of the operation, given the predicted operation characteristics <b>40</b>, in the same manner as the first embodiment <b>20</b>. Alternatively, the method in this embodiment <b>21</b> may determine these operation effects <b>46</b> by calculating values based on predicted operation characteristics <b>40</b> and operation profile <b>36</b>. For example, multiplying fuel consumption, an operation characteristic <b>40</b>, by fuel price, an operation profile <b>36</b> parameter, predicts fuel cost for the operation.

0021The final step <b>60</b> of both the first embodiment <b>20</b> and second embodiment <b>21</b> is to predict operation suitability <b>6</b> at the node <b>12</b> for several points in time based on the predicted values for the operation variables <b>8</b>. For clarity, the operation variables <b>8</b> include weather conditions <b>24</b>, crop conditions <b>104</b>, soil conditions <b>28</b>, soil characteristics <b>52</b>, operation characteristics <b>40</b>, and operation effects <b>46</b>. <figref idref="DRAWINGS">FIG. 4</figref> illustrates a table <b>62</b> showing input and output for a harvest operation suitability algorithm <b>64</b>. By selecting suitability parameters <b>65</b>, the suitability algorithm <b>64</b> calculates suitability values for each operation variable <b>6</b> based on the corresponding suitability parameters <b>66</b>. These parameters <b>66</b> define thresholds at which the operation variable is suitable <b>68</b> for the crop harvesting operation, and thresholds beyond which the variable is unsuitable <b>70</b>.

0022For example, if a value for an operation variable <b>8</b> at a given point in time falls within the suitable value thresholds <b>68</b>, then the suitability value <b>6</b>′ for that operation variable <b>8</b> is 100%. Conversely, if the value for the variable <b>8</b> falls outside of the unsuitable value thresholds <b>70</b>, then the suitability value <b>6</b>′ for that operation variable <b>8</b> is 0%. Finally, if the value for the operation variable <b>8</b> falls within the transition range between suitable and unsuitable thresholds, then the suitability value <b>6</b>′ for that operation variable <b>8</b> is the fraction between the suitable threshold value <b>68</b> and unsuitable threshold value <b>70</b>. <figref idref="DRAWINGS">FIG. 4</figref> illustrates an example, with suitability parameters <b>66</b> for crop moisture having a suitable upper threshold value of 24%, and an unsuitable upper threshold value of 28%. Thus, for the predicted crop moisture I content of 26%, the suitability value <b>6</b>′ for crop moisture content calculates as ((26−24)/(28−14))×100=50%.

0023As illustrated, the suitability <b>66</b> parameters also include weightings <b>72</b> emphasizing relative importance of the operation variables <b>8</b> in assessing overall operation suitability <b>6</b> for the node <b>12</b>. The suitability algorithm <b>64</b> calculates overall suitability <b>6</b> by multiplying each operation variable suitability value <b>6</b>′ by its corresponding weighting <b>72</b> for a weighted suitability value, then dividing the sum of the weighted suitability values by the sum of the weighting values <b>72</b>. <figref idref="DRAWINGS">FIG. 4</figref> illustrates an example of overall node suitability <b>6</b> for performance of a crop harvesting operation, based on predicted weather conditions <b>24</b>, crop conditions <b>104</b>, and operation characteristics <b>40</b>.

0024Values for operation variables <b>8</b>, operation variable suitability <b>6</b>′, and overall node suitability <b>6</b> generated from the foregoing method are available for display <b>80</b> in numerous forms. <figref idref="DRAWINGS">FIG. 5</figref> shows an example of a map display <b>80</b> showing overall node suitability <b>6</b> for a crop harvesting operation over an entire farm field <b>10</b> on a single day. This figure also shows a summary of operation suitability <b>6</b> over the entire field <b>10</b> in a bar graph <b>82</b> at the bottom of the illustration. <figref idref="DRAWINGS">FIG. 6</figref> shows a similar bar graph display <b>84</b> showing overall node suitability <b>6</b>, but for multiple days in the farm field <b>10</b>. This display <b>84</b> is especially useful when planning the best day for performance of a crop harvesting operation. Finally, <figref idref="DRAWINGS">FIG. 7</figref> illustrates a bar graph display <b>86</b> showing overall node suitability <b>6</b> for multiple farm fields <b>10</b> on a single day. This display <b>86</b> is especially useful in selecting alternative fields <b>10</b> in which to perform the operation on a given day. It is of interest to note that a field <b>10</b> may never be suitable for performance of a particular type of soil engaging operation, given the predicted weather conditions <b>24</b>, crop conditions <b>104</b>, and soil conditions <b>28</b>. Thus, this method becomes useful to assess economic impact of harvest operation timing, irrespective of suitability.

0025Having described the illustrated embodiments, it will become apparent that various modifications can be made without departing from the scope of the invention as defined in the accompanying claims.